In our experimental conditions, we are[unreadable] determining "colocalization" in the resolution limit of light microscopy. In this PPG, the "colocalization" term[unreadable] will imply that two molecules may share the same microenvironment and/or can be elements of the same set[unreadable] of macromolecular complexes which are resolved by the optical system. Forexample, two proteins forming part[unreadable] of two different molecular complexes may show positive "colocalization" tests due to the lack of the optical resolution[unreadable] to detect each molecular complex. Still, the positive test will indicate that the two proteins are located proximally[unreadable] within the optical resolution. Thus, in "colocalization" determinations it is critical to achieve the maximal x,y,z[unreadable] resolution. In this PPG, we will use developed methodologies in the laboratory to achieve the highest resolution[unreadable] attainable with optical microscopy. We have maximized the optical linearity and the quantity of light recorded by the[unreadable] detectors in conjunction with image restoration analysis to recover the light lost by the optical imperfections of the[unreadable] system. This methodology has allowed us to reach a resolution in the x,y plane of about 100-200 nm (see Figs. 1-3).[unreadable] Definition of "colocalization" and random "colocalization". In a cell labeled with two fluorophores, one red (R)[unreadable] and one green (G), the number of voxels above threshold that are labeled with the red fluorophore is nRand the[unreadable] number labeled with the green fluorophore is nG. The number of colocalized voxels, i.e. those voxels containing[unreadable] intensity signals above threshold from both fluorophores is ncoioc. The percentage of "colocalization" of G with R is[unreadable] given by 100*^^- (eq. 1); and the percentage of "colocalization" of R with G is given by 100*^^ (eq. 2). In[unreadable] n* nc[unreadable] these measurements we will estimate the probability that the measured "colocalization" could occur by chance by[unreadable] calculating % random "colocalization" from ((area_1x area_2)/total areaA2) x100, where area_1 and area_2 are the[unreadable] areas above threshold (eq. 3).[unreadable] The "colocalization" method is generally based on the user ability to set the intensity threshold, and has the major[unreadable] draw back of the lack of a mathematical model to adjust the threshold of both images that will define the degree of[unreadable] paired pixel overlap. Most importantly the pixel overlap method has the limitation of being a binary test that[unreadable] determines whether the two paired pixels in two images have or not intensities above the intensity threshold and does[unreadable] not consider whether the two stained proteins have a landscape of intensity staining with a high degree of correlation,[unreadable] as it would be expected if they are elements of a common complex. The application of a correlation measurement[unreadable] was recently described by Li et al. (2004)7.[unreadable] In the following sections, I will discuss and apply developed algorithms to quantify the degree of protein-protein[unreadable] association by two methods, the INTENSITY CORRELATION ANALYSIS and the INTENSITY THRESHOLD[unreadable] "COLOCALIZATION"ANALYSIS.[unreadable] Intensity correlation analysis. In the present analysis, we acquire high resolution images to compare the[unreadable] correlation of pixel intensities in equivalent x,y coordinates of paired images from cells double stained for two different[unreadable] proteins. The prediction is that if two proteins are elements of the same macromolecular complex, the intensity[unreadable] staining landscape of the two images should have a x,y pixel to pixel positive correlation. On the contrary, if the two[unreadable] proteins are localized in distinct compartments the result will be a negative correlation. Finally, if the proteins in the[unreadable] two images are labeled in a diffuse non structured pattern (random), the correlation will tend to 0.[unreadable] This method is based on the principle that for any set of values the sum of the differences from the mean equal zero,[unreadable] i.e., ?N(A-a)=0, where a is the mean of the distribution with N values of Al. In the experiment N is the number of[unreadable] pixels, and A is the intensity for each pixel. If we have two set of values in two arrays 1 and 2 with N pixels per array[unreadable] having a random distribution of intensities A-,and 6, for arrays 1 and 2 , the sum of the product of their differences will[unreadable] also tend to zero, thus IN(A-a)(S,-Jb)~0. On the other hand, if the two intensities are positively correlated, the product[unreadable] will tend to be a positive value (^(Ara)(Brb}>G) and if they are negatively correlated the product will tend to a[unreadable] negative value (LN(Ara)(Brb)<0).